Intrusion detection : a data mining approach

• 作者： Sengupta, Nandita, author.
• 其他作者：
  • Sil, Jaya, author.
  • SpringerLink (Online service)
• 其他題名：
  • Cognitive intelligence and robotics.
• 出版： Singapore : Springer Singapore :Imprint: Springer
• 叢書名： Cognitive intelligence and robotics,
• 主題： Intrusion detection systems (Computer security) , Data mining. , Computer Communication Networks. , Systems and Data Security. , Cryptology.
• ISBN： 9789811527166 (electronic bk.) 、 9789811527159 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Chapter 1. Introduction -- Chapter 2. Discretization -- Chapter 3. Data Reduction -- Chapter 4. Q-Learning Classifiers -- Chapter 5. Hierarchical Q - Learning Classifier -- Chapter 6. Conclusions and Future Research.
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• 摘要註: This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion. The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.
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